论文标题
对实施常见认知模型的体系结构的类似概念记忆
Analogical Concept Memory for Architectures Implementing the Common Model of Cognition
论文作者
论文摘要
实施认知常见模型的体系结构 - SOAR，ACT -R和Sigma-在认知建模以及设计复杂的智能代理方面的研究中都有明显的位置。在本文中，我们探讨了如何将类似过程的计算模型带入这些体系结构中，以从互动获得的示例中启用概念获取。我们提出了一种新的类似概念记忆，以增强其当前声明性的长期记忆系统。我们将概念学习的问题构成了嵌入在交互式任务学习（ITL）和体现语言处理（ELP）的较大上下文中的问题。我们证明，在提出的记忆中实施的类似学习方法可以迅速学习一种多种类型的新颖概念，这些概念不仅在识别环境中的概念，而且在行动选择中有用。我们的方法已在实施的认知系统Aileen中实例化，并在模拟机器人域进行了评估。
Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intelligent agents. In this paper, we explore how computational models of analogical processing can be brought into these architectures to enable concept acquisition from examples obtained interactively. We propose a new analogical concept memory for Soar that augments its current system of declarative long-term memories. We frame the problem of concept learning as embedded within the larger context of interactive task learning (ITL) and embodied language processing (ELP). We demonstrate that the analogical learning methods implemented in the proposed memory can quickly learn a diverse types of novel concepts that are useful not only in recognition of a concept in the environment but also in action selection. Our approach has been instantiated in an implemented cognitive system AILEEN and evaluated on a simulated robotic domain.
